A pretrained aircraft type classifier that sorts an image into one of 10 categories — what type of aircraft it is. Use the aircraft type API immediately, no training required, then adapt it to your own data when you need more.
Drop in a photo and get the prediction back. No signup, no setup.
A sample of the 16 labels this pretrained classifier chooses between.
Need a label that isn't here? Clone the classifier into your Nyckel console and edit the label set to fit your data.
Once you've added this classifier to your console, you get your own copy of it behind your own endpoint. Invoke it with any HTTP client:
curl
curl -X POST "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke" \
-H "Authorization: Bearer $NYCKEL_ACCESS_TOKEN" \
-H "Content-Type: application/json" \
-d '{"data": "https://example.com/photo.jpg"}'Python
import requests
# Get an access token: https://www.nyckel.com/docs/api/overview/authentication/
token = "YOUR_ACCESS_TOKEN"
response = requests.post(
"https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke",
headers={"Authorization": "Bearer " + token},
json={"data": "https://example.com/photo.jpg"},
)
print(response.json())Example response
{
"labelName": "Bomber",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 aircraft type categories, served on Nyckel's own infrastructure — your image stays on Nyckel.
Send an image URL or file to the invoke endpoint; the response is a label with a confidence score.
Clone it, then correct predictions and add your own samples in the console — Nyckel retrains automatically, turning this into a custom model tuned to your data.
Implementing an aircraft type identifier can help airport security personnel quickly recognize and verify the aircraft models approaching the runway. This can lead to faster decision-making processes, ensuring that appropriate security measures are in place for different types of aircraft.
Airlines can utilize this classification function to monitor and manage their fleet more effectively by categorizing aircraft types for maintenance schedules and operational planning. This ensures that maintenance resources are allocated efficiently, minimizing aircraft downtime and maximizing operational readiness.
Insurance companies could leverage aircraft type identification to assess risk levels and determine suitable coverage options for various aircraft models. This transparency will allow for more accurate premium pricing and potentially lower rates for safe aircraft types.
Airlines can tailor their marketing efforts based on the identified types of aircraft used in flight operations. This could lead to more targeted advertising and promotions, as different aircraft types may appeal to different customer segments, such as luxury travelers or budget-conscious flyers.
In the event of an aviation incident, investigators can use aircraft type identification to quickly categorize involved aircraft, aiding in the investigation process. This function can facilitate access to specific aircraft data and safety records relevant to the incident analysis.
Environmental agencies can utilize the aircraft type identifier to assess and monitor the ecological impact of different aircraft models. This data can help in regulatory decisions and initiatives aimed at reducing carbon emissions and improving sustainability in aviation.
Airlines can enhance their flight path planning and air traffic control operations by utilizing aircraft type identifiers to better allocate airspace resources. This ensures that flight paths take into account the different performance characteristics of various aircraft types, resulting in more efficient air traffic management.
A zero-shot classifier uses a large foundation model's general knowledge to pick between your labels — no task-specific training, so new or edited labels work immediately. A Nyckel-trained classifier has been trained on labeled examples and runs on Nyckel's own infrastructure, which typically makes it faster, cheaper per call, and more accurate on data that resembles its training set. The "Under the hood" section on this page shows which kind this classifier is, and any classifier can be adapted into a trained one by adding your own examples.
Honestly: we can't know in advance — it depends on your data stream and how closely it resembles what this classifier has seen. The reliable way to find out is to measure it on your own data: start invoking the classifier with real traffic, or upload and annotate a set of images in the console — make sure they look like your production data, not idealized examples. Nyckel's evaluation metrics then show you exactly how it performs on that data before you rely on it.
No classifier is perfect, so Nyckel is built around the correction loop: invokes can be captured for review, you confirm or correct predictions in the console, and corrections become training data. Over time the model adapts to your data distribution — accuracy on your traffic improves with use rather than staying fixed.
No. This aircraft type classifier works out of the box — clone it into your console and you'll have your own API endpoint in under a minute. Training data only enters the picture when you want to adapt it: your corrected predictions and uploaded samples improve the model, and you can also edit the label set to match your needs.
Trying the classifier on this page is free with no signup. Cloning it requires a free account, and the free tier covers your first API calls each month — see nyckel.com/pricing for current limits and paid tiers.
Add this pretrained classifier to your Nyckel console — you'll get a live API endpoint in under a minute, and a path to a custom model when you need one.